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Automatic extraction of cell nuclei from H&E-stained histopathological images
Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478972/ https://www.ncbi.nlm.nih.gov/pubmed/28653017 http://dx.doi.org/10.1117/1.JMI.4.2.027502 |
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author | Yi, Faliu Huang, Junzhou Yang, Lin Xie, Yang Xiao, Guanghua |
author_facet | Yi, Faliu Huang, Junzhou Yang, Lin Xie, Yang Xiao, Guanghua |
author_sort | Yi, Faliu |
collection | PubMed |
description | Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei. |
format | Online Article Text |
id | pubmed-5478972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
spelling | pubmed-54789722018-06-21 Automatic extraction of cell nuclei from H&E-stained histopathological images Yi, Faliu Huang, Junzhou Yang, Lin Xie, Yang Xiao, Guanghua J Med Imaging (Bellingham) Digital Pathology Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei. Society of Photo-Optical Instrumentation Engineers 2017-06-21 2017-04 /pmc/articles/PMC5478972/ /pubmed/28653017 http://dx.doi.org/10.1117/1.JMI.4.2.027502 Text en © The Authors. https://creativecommons.org/licenses/by/3.0/ Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
spellingShingle | Digital Pathology Yi, Faliu Huang, Junzhou Yang, Lin Xie, Yang Xiao, Guanghua Automatic extraction of cell nuclei from H&E-stained histopathological images |
title | Automatic extraction of cell nuclei from H&E-stained histopathological images |
title_full | Automatic extraction of cell nuclei from H&E-stained histopathological images |
title_fullStr | Automatic extraction of cell nuclei from H&E-stained histopathological images |
title_full_unstemmed | Automatic extraction of cell nuclei from H&E-stained histopathological images |
title_short | Automatic extraction of cell nuclei from H&E-stained histopathological images |
title_sort | automatic extraction of cell nuclei from h&e-stained histopathological images |
topic | Digital Pathology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478972/ https://www.ncbi.nlm.nih.gov/pubmed/28653017 http://dx.doi.org/10.1117/1.JMI.4.2.027502 |
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